def assign_grouping(self, op, op_reg_manager):
"""Assign grouping to the given op and updates the manager.
Args:
op: tf.Operation to assign grouping to.
op_reg_manager: OpRegularizerManager to keep track of the grouping.
"""
# Check if all input ops have groups, or tell the manager to process them.
input_ops = op_handler_util.get_input_ops(op, op_reg_manager)
input_ops_without_group = op_handler_util.get_ops_without_groups(
input_ops, op_reg_manager)
# Check if all output ops have groups, or tell the manager to process them.
output_ops = op_handler_util.get_output_ops(op, op_reg_manager)
output_ops_without_group = op_handler_util.get_ops_without_groups(
output_ops, op_reg_manager)
# Remove non-passthrough ops from outputs ops to group with.
output_ops = op_handler_util.remove_non_passthrough_ops(
output_ops, op_reg_manager)
# Only group with ops that have the same size. Process the ops that have
# mismatched size.
input_ops_to_group, input_ops_to_process = (
op_handler_util.separate_same_size_ops(op, input_ops))
output_ops_to_group, output_ops_to_process = (
op_handler_util.separate_same_size_ops(op, output_ops))
# Remove broadcast ops.
input_ops_to_process = [input_op for input_op in input_ops_to_process
if not self._is_broadcast(input_op, op_reg_manager)]
# Also process ungrouped ops.
for input_op_without_group in input_ops_without_group:
if input_op_without_group not in input_ops_to_process:
input_ops_to_process.append(input_op_without_group)
for output_op_without_group in output_ops_without_group:
if output_op_without_group not in output_ops_to_process:
output_ops_to_process.append(output_op_without_group)
# Align op slice sizes if needed.
op_slices = op_reg_manager.get_op_slices(op)
input_op_slices = op_handler_util.get_op_slices(
input_ops_to_group, op_reg_manager)
output_op_slices = op_handler_util.get_op_slices(
output_ops_to_group, op_reg_manager)
aligned_op_slice_sizes = op_handler_util.get_aligned_op_slice_sizes(
op_slices, input_op_slices, output_op_slices)
op_handler_util.reslice_ops(input_ops_to_group + [op] + output_ops_to_group,
aligned_op_slice_sizes, op_reg_manager)
# Repopulate OpSlice data, as ops may have been resliced.
input_op_slices, output_op_slices = self._get_input_output_op_slices(
input_ops_to_group, output_ops_to_group, op_reg_manager)
# Group with inputs and outputs.
op_handler_util.group_aligned_input_output_slices(
op, input_ops_to_process, output_ops_to_process, input_op_slices,
output_op_slices, aligned_op_slice_sizes, op_reg_manager)
def assign_grouping(self, op, op_reg_manager):
"""Assign grouping to the given op and updates the manager.
Args:
op: tf.Operation to assign grouping to.
op_reg_manager: OpRegularizerManager to keep track of the grouping.
"""
# This is a source op so begin by getting the OpGroup or creating one.
op_slices = op_reg_manager.get_op_slices(op)
for op_slice in op_slices:
op_group = op_reg_manager.get_op_group(op_slice)
if op_group is None:
op_reg_manager.create_op_group_for_op_slice(op_slice)
# Check if all input ops have groups, or tell the manager to process them.
input_ops = op_handler_util.get_input_ops(op, op_reg_manager)
input_ops_without_group = op_handler_util.get_ops_without_groups(
input_ops, op_reg_manager)
# Check if all output ops have groups, or tell the manager to process them.
output_ops = op_handler_util.get_output_ops(op, op_reg_manager)
output_ops_without_group = op_handler_util.get_ops_without_groups(
output_ops, op_reg_manager)
# Remove non-passthrough ops from outputs ops to group with.
output_ops = op_handler_util.remove_non_passthrough_ops(
output_ops, op_reg_manager)
# Only group with ops that have the same size. Process the ops that have
# mismatched size.
output_ops_to_group, output_ops_to_process = (
op_handler_util.separate_same_size_ops(op, output_ops))
# Also process ungrouped ops.
input_ops_to_process = input_ops_without_group
output_ops_to_process.extend(output_ops_without_group)
# Align op slice sizes if needed.
output_op_slices = op_handler_util.get_op_slices(
output_ops_to_group, op_reg_manager)
aligned_op_slice_sizes = op_handler_util.get_aligned_op_slice_sizes(
op_slices, [], output_op_slices)
op_handler_util.reslice_ops([op] + output_ops_to_group,
aligned_op_slice_sizes, op_reg_manager)
# Repopulate OpSlice data, as ops may have been resliced.
output_op_slices = op_handler_util.get_op_slices(
output_ops_to_group, op_reg_manager)
# Group with outputs.
op_handler_util.group_op_with_inputs_and_outputs(
op, [], output_op_slices, aligned_op_slice_sizes, op_reg_manager)
# Reprocess ops.
op_reg_manager.process_ops(output_ops_to_process +
input_ops_to_process)
def assign_grouping(self, op, op_reg_manager):
"""Assign grouping to the given op and updates the manager.
Args:
op: tf.Operation to assign grouping to.
op_reg_manager: OpRegularizerManager to keep track of the grouping.
"""
# TODO(a1): Consider refactoring this duplicated logic.
# Check if all input ops have groups, or tell the manager to process them.
input_ops = op_handler_util.get_input_ops(op, op_reg_manager)
input_ops_without_group = op_handler_util.get_ops_without_groups(
input_ops, op_reg_manager)
# Check if all output ops have groups, or tell the manager to process them.
output_ops = op_handler_util.get_output_ops(op, op_reg_manager)
output_ops_without_group = op_handler_util.get_ops_without_groups(
output_ops, op_reg_manager)
# Remove non-passthrough ops from outputs ops to group with.
output_ops = op_handler_util.remove_non_passthrough_ops(
output_ops, op_reg_manager)
# Only group with output ops that have the same size. Process the ops that
# have mismatched size.
input_ops_to_group = input_ops
input_ops_to_process = input_ops_without_group
output_ops_to_group, output_ops_to_process = (
op_handler_util.separate_same_size_ops(op, output_ops))
# Also process ungrouped ops.
output_ops_to_process.extend(output_ops_without_group)
# Populate OpSlice data for all relevant ops.
concat_op_slices = op_reg_manager.get_op_slices(op)
input_op_slices, output_op_slices = self._get_input_output_op_slices(
input_ops_to_group, output_ops_to_group, op_reg_manager)
# Align op slices sizes if needed.
aligned_op_slice_sizes = op_handler_util.get_aligned_op_slice_sizes(
concat_op_slices, input_op_slices, output_op_slices)
op_handler_util.reslice_concat_ops(input_ops_to_group,
aligned_op_slice_sizes,
op_reg_manager)
op_handler_util.reslice_ops(output_ops_to_group + [op],
aligned_op_slice_sizes, op_reg_manager)
# Repopulate OpSlice data, as ops may have been resliced.
input_op_slices, output_op_slices = self._get_input_output_op_slices(
input_ops_to_group, output_ops_to_group, op_reg_manager)
# Group aligned OpSlice.
op_handler_util.group_aligned_input_output_slices(
op, input_ops_to_process, output_ops_to_process, input_op_slices,
output_op_slices, aligned_op_slice_sizes, op_reg_manager)
def assign_grouping(self, op, op_reg_manager):
"""Assign grouping to the given op and updates the manager.
Args:
op: tf.Operation to assign grouping to.
op_reg_manager: OpRegularizerManager to keep track of the grouping.
"""
concat_axis = _get_concat_op_axis(op)
# Need to figure out the rank to know if axis is last.
rank = len(op.inputs[0].shape) # Rank of the first input.
if concat_axis != -1 and concat_axis != rank - 1:
# Concat is actually grouping inputs!
handler = grouping_op_handler.GroupingOpHandler()
handler.assign_grouping(op, op_reg_manager)
return
# If concat is of the last dimension, this is a `standard` concat.
# TODO(a1): Consider refactoring this duplicated logic.
# Check if all input ops have groups, or tell the manager to process them.
input_ops = op_handler_util.get_input_ops(op, op_reg_manager)
input_ops_without_group = op_handler_util.get_ops_without_groups(
input_ops, op_reg_manager)
# Check if all output ops have groups, or tell the manager to process them.
output_ops = op_handler_util.get_output_ops(op, op_reg_manager)
output_ops_without_group = op_handler_util.get_ops_without_groups(
output_ops, op_reg_manager)
# Remove non-passthrough ops from outputs ops to group with.
output_ops = op_handler_util.remove_non_passthrough_ops(
output_ops, op_reg_manager)
# Only group with output ops that have the same size. Process the ops that
# have mismatched size.
input_ops_to_group = input_ops
input_ops_to_process = input_ops_without_group
output_ops_to_group, output_ops_to_process = (
op_handler_util.separate_same_size_ops(op, output_ops))
# Also process ungrouped ops.
output_ops_to_process.extend(output_ops_without_group)
# Populate OpSlice data for all relevant ops.
concat_op_slices = op_reg_manager.get_op_slices(op)
input_op_slices, output_op_slices = self._get_input_output_op_slices(
input_ops_to_group, output_ops_to_group, op_reg_manager)
# Align op slices sizes if needed.
aligned_op_slice_sizes = op_handler_util.get_aligned_op_slice_sizes(
concat_op_slices, input_op_slices, output_op_slices)
op_handler_util.reslice_concat_ops(input_ops_to_group,
aligned_op_slice_sizes,
op_reg_manager)
op_handler_util.reslice_ops(output_ops_to_group + [op],
aligned_op_slice_sizes, op_reg_manager)
# Repopulate OpSlice data, as ops may have been resliced.
input_op_slices, output_op_slices = self._get_input_output_op_slices(
input_ops_to_group, output_ops_to_group, op_reg_manager)
# Group aligned OpSlice.
op_handler_util.group_aligned_input_output_slices(
op, input_ops_to_process, output_ops_to_process, input_op_slices,
output_op_slices, aligned_op_slice_sizes, op_reg_manager)
